Inductive reasoning and its underlying structure: Support for difficulty and item position effects

This paper reports an investigation of the influence of method effects on the measurement of reasoning and of the relationships of these effects to basic cognitive processes. For this purpose, the variation due to the item-position and difficulty effects was separated from the variation due to the measured latent source of inductive reasoning. Data were collected by means of inductive reasoning items and cognitive tasks measuring working memory (WM) updating, rule learning, and automatization. Confirmatory factor analysis models served the decomposition of the variation of inductive reasoning data into a purified version of inductive reasoning, item-position, and difficulty components. The investigation of the relationships of corresponding latent variables and basic cognitive processes revealed two major associations: (a) the purified version of reasoning correlated with WM updating and (b) the item-position effect correlated with variants of learning. These results could be interpreted as signifying a two-dimensional structure of reasoning associated with executive functioning and learning processes

The dual mechanisms of cognitive control and their relation to reasoning and the item-position effect

Braver's (2012) dual mechanisms of cognitive control differentiate between proactive control (PMC; i.e. early selection and maintenance of goal-relevant information) and reactive control (RMC; i.e. a late mobilization of attention when required). It has been suggested that higher cognitive capacities (as indicated by reasoning ability as a major characteristic of fluid intelligence) facilitate using the more resource-demanding PMC. We propose the following alternative explanation: engagement in PMC during the completion of reasoning tests leads to better test performance because gained knowledge (i.e. rules learned) during completion of early items is better maintained and transferred to later items. This learning of rules during the completion of a reasoning test results in an item-position effect (IPE) as an additional source of individual differences besides reasoning ability. We investigated this idea in a sample of 210 young adults who completed the AX-Continuous Performance Task (AX-CPT) and the Vienna Matrices Test (VMT). Using fixed-links modeling, we separated an IPE from reasoning ability in the VMT. Based on reaction time (RT) patterns across AX-CPT conditions, we identified three different groups by means of latent-profile analysis. RT patterns indicated engagement in PMC for Group A, mixed PMC and RMC for Group B, and RMC for Group C. With the consideration of the IPE, groups did not differ in their reasoning abilities. However, Group A (engaging in PMC) had a more pronounced IPE than Group C (engaging in RMC). Therefore, we conclude that PMC contributes to a stronger IPE, which in turn leads to higher scores in reasoning tests as measures of fluid intelligence

Spatial variability - so what?

Since the landmark papers of Conway and Abrahamson many studies have tried to quantify spatial variability. Many different methods have been used and the studies covered a variety of scales. Accordingly, some results appear contradictory, suggesting that the degree of spatial variation varies widely. This is not surprising, and is partly due to the methodology used and of course, due to varying natural conditions. Spatial variability is doubtless an inherent property of the snowpack. One important result seems to be that the layering is less variable than, for example, the stability of small column tests. Whereas it is often perceived that the results of the studies were not conclusive, it seems clear that they completely changed our view of spatial variability. We realized the importance of scale issues. For example, the variation will strongly depend on the measurement scale – the so-called support – of the method (SnowMicroPen vs. compression test vs. rutschblock test). Geostatistical analysis has been intro duced and used to derive appropriate input data for numerical models. Model results suggest that spatial variation of strength properties have a substantial knockdown effect on slope stability and that the effect increases with increasing spatial correlation. The focus on scale has also revealed that spatial variations can promote instability or inhibit it. With the awareness of scale we can now address the causes of spatial variability. Many processes such as radiation and wind act at several scales. The most challenging process is probably wind that might hinder prediction of variability at the slope scale. However, at the regional scale, already today, many avalanche forecasting services try to address differences in respect to slope aspect. We will review the present state of knowledge, discuss consequences for avalanche forecasting and snow stability evaluation, and recommend future research directions